Anomaly detection

A method of processing input data, the method comprising:

• obtaining input data based on sensor data obtained during manufacturing of an article using one or more manufacturing recipes in a plurality of manufacturing equipment or using a plurality of manufacturing recipes in one or more manufacturing equipment;
• feeding the input data and additional information for identifying one or more contexts about the input data into a neural network model loaded on a computer device, wherein the neural network model, including an encoder and a decoder, is trained with the input data and the additional information;
• feeding a context indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a first preprocessing neural network model;
• processing the context indicator using the first preprocessing neural network model;
• further feeding a preprocessed context indicator which is an output of the first preprocessing neural network model, into the neural network model,
• wherein the preprocessed context indicator is a dense representation of the context indicator;
• generating an output by processing the input data using the neural network model based on the additional information about the input data, wherein the neural network model processes the input data for different contexts of the input data identified by the additional information;
• and
• detecting an anomaly for a plurality of normal states corresponding to the input data based on the output of the neural network model.

A computing device for processing input data, comprising:

• one or more processors;
• and
• a memory for storing computer programs executable on the one or more processors;
• wherein the one or more processors are configured to:
• obtain input data based on sensor data obtained during manufacturing of an article using one or more manufacturing recipes in a plurality of manufacturing equipment or using a plurality of manufacturing recipes in one or more manufacturing equipment;
• feed the input data and additional information for identifying one or more contexts about the input data into a neural network model loaded on a computer device, wherein the neural network model, including an encoder and a decoder, is trained with the input data and the additional information;
• feed a context indicator that associates the input data with at least one of a characteristic of one manufacturing recipe of the one or more manufacturing recipes or a characteristic of one manufacturing equipment of the one or more manufacturing equipment, into a first preprocessing neural network model;
• process the context indicator using the first preprocessing neural network model;
• further feed a preprocessed context indicator which is an output of the first preprocessing neural network model, into the neural network model,
• wherein the preprocessed context indicator is a dense representation of the context indicator;
• generate an output by processing the input data using the neural network model based on the additional information about the input data, wherein the neural network model processes the input data for different contexts of the input data identified by the additional information;
• and
• detect an anomaly for a plurality of normal states corresponding to the input data based on the output of the neural network model.